




"""simple docstring"""







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import YolosImageProcessor






class    SCREAMING_SNAKE_CASE__       (       unittest.TestCase       ):


						def __init__(				self					:      str    ,    lowerCAmelCase					:      int    ,    lowerCAmelCase					:      List[Any]=7    ,    lowerCAmelCase					:      Optional[int]=3    ,    lowerCAmelCase					:      Dict=30    ,    lowerCAmelCase					:      Dict=400    ,    lowerCAmelCase					:      Tuple=True    ,    lowerCAmelCase					:      Union[str, Any]=None    ,    lowerCAmelCase					:      int=True    ,    lowerCAmelCase					:      Optional[Any]=[0.5, 0.5, 0.5]    ,    lowerCAmelCase					:      Any=[0.5, 0.5, 0.5]    ,    lowerCAmelCase					:      Optional[int]=True    ,    lowerCAmelCase					:      int=1 / 255    ,    lowerCAmelCase					:      Any=True    ,    ):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowerCAmelCase						=					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
										lowerCAmelCase						=					parent
										lowerCAmelCase						=					batch_size
										lowerCAmelCase						=					num_channels
										lowerCAmelCase						=					min_resolution
										lowerCAmelCase						=					max_resolution
										lowerCAmelCase						=					do_resize
										lowerCAmelCase						=					size
										lowerCAmelCase						=					do_normalize
										lowerCAmelCase						=					image_mean
										lowerCAmelCase						=					image_std
										lowerCAmelCase						=					do_rescale
										lowerCAmelCase						=					rescale_factor
										lowerCAmelCase						=					do_pad


						def 	__lowercase			(				self					:      Optional[Any]       ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}




						def 	__lowercase			(				self					:      Dict    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      Tuple=False       ):
										if not batched:
														lowerCAmelCase						=					image_inputs[0]
														if isinstance(__snake_case    ,    Image.Image       ):
																		lowerCAmelCase						=					image.size
														else:
																		lowerCAmelCase						=					image.shape[1], image.shape[2]
														if w < h:
																		lowerCAmelCase						=					int(self.size["""shortest_edge"""] * h / w       )
																		lowerCAmelCase						=					self.size['shortest_edge']
														elif w > h:
																		lowerCAmelCase						=					self.size['shortest_edge']
																		lowerCAmelCase						=					int(self.size["""shortest_edge"""] * w / h       )
														else:
																		lowerCAmelCase						=					self.size['shortest_edge']
																		lowerCAmelCase						=					self.size['shortest_edge']

										else:
														lowerCAmelCase						=					[]
														for image in image_inputs:
																		lowerCAmelCase						=					self.get_expected_values([image]       )
																		expected_values.append((expected_height, expected_width)       )
														lowerCAmelCase						=					max(__snake_case    ,    key=lambda lowerCAmelCase       : item[0]       )[0]
														lowerCAmelCase						=					max(__snake_case    ,    key=lambda lowerCAmelCase       : item[1]       )[1]

										return expected_height, expected_width






@require_torch
@require_vision
class    SCREAMING_SNAKE_CASE__       (       lowerCamelCase__    ,					unittest.TestCase       ):
						_a         =    YolosImageProcessor if is_vision_available() else None


						def 	__lowercase			(				self					:      Dict       ):
										lowerCAmelCase						=					YolosImageProcessingTester(self       )


						@property
						def 	__lowercase			(				self					:      Optional[Any]       ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 	__lowercase			(				self					:      str       ):
										lowerCAmelCase						=					self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(__snake_case    ,    """image_mean"""       )       )
										self.assertTrue(hasattr(__snake_case    ,    """image_std"""       )       )
										self.assertTrue(hasattr(__snake_case    ,    """do_normalize"""       )       )
										self.assertTrue(hasattr(__snake_case    ,    """do_resize"""       )       )
										self.assertTrue(hasattr(__snake_case    ,    """size"""       )       )


						def 	__lowercase			(				self					:      Dict       ):
										lowerCAmelCase						=					self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size    ,    {"""shortest_edge""": 18, """longest_edge""": 1333}       )
										self.assertEqual(image_processor.do_pad    ,    __snake_case       )

										lowerCAmelCase						=					self.image_processing_class.from_dict(
										    self.image_processor_dict    ,    size=42    ,    max_size=84    ,    pad_and_return_pixel_mask=__snake_case       )
										self.assertEqual(image_processor.size    ,    {"""shortest_edge""": 42, """longest_edge""": 84}       )
										self.assertEqual(image_processor.do_pad    ,    __snake_case       )


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										pass


						def 	__lowercase			(				self					:      str       ):
										# Initialize image_processing
										lowerCAmelCase						=					self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										lowerCAmelCase						=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__snake_case       )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    ,    Image.Image       )

										# Test not batched input
										lowerCAmelCase						=					image_processing(image_inputs[0]    ,    return_tensors="""pt"""       ).pixel_values

										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case       )

										self.assertEqual(
										    encoded_images.shape    ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,    )

										# Test batched
										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case    ,    batched=__snake_case       )

										lowerCAmelCase						=					image_processing(__snake_case    ,    return_tensors="""pt"""       ).pixel_values
										self.assertEqual(
										    encoded_images.shape    ,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,    )


						def 	__lowercase			(				self					:      Dict       ):
										# Initialize image_processing
										lowerCAmelCase						=					self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										lowerCAmelCase						=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__snake_case    ,    numpify=__snake_case       )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    ,    np.ndarray       )

										# Test not batched input
										lowerCAmelCase						=					image_processing(image_inputs[0]    ,    return_tensors="""pt"""       ).pixel_values

										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case       )

										self.assertEqual(
										    encoded_images.shape    ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,    )

										# Test batched
										lowerCAmelCase						=					image_processing(__snake_case    ,    return_tensors="""pt"""       ).pixel_values

										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case    ,    batched=__snake_case       )

										self.assertEqual(
										    encoded_images.shape    ,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,    )


						def 	__lowercase			(				self					:      Optional[Any]       ):
										# Initialize image_processing
										lowerCAmelCase						=					self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										lowerCAmelCase						=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__snake_case    ,    torchify=__snake_case       )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    ,    torch.Tensor       )

										# Test not batched input
										lowerCAmelCase						=					image_processing(image_inputs[0]    ,    return_tensors="""pt"""       ).pixel_values

										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case       )

										self.assertEqual(
										    encoded_images.shape    ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,    )

										# Test batched
										lowerCAmelCase						=					image_processing(__snake_case    ,    return_tensors="""pt"""       ).pixel_values

										lowerCAmelCase						=					self.image_processor_tester.get_expected_values(__snake_case    ,    batched=__snake_case       )

										self.assertEqual(
										    encoded_images.shape    ,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,    )


						def 	__lowercase			(				self					:      List[Any]       ):
										# Initialize image_processings
										lowerCAmelCase						=					self.image_processing_class(**self.image_processor_dict       )
										lowerCAmelCase						=					self.image_processing_class(do_resize=__snake_case    ,    do_normalize=__snake_case    ,    do_rescale=__snake_case       )
										# create random PyTorch tensors
										lowerCAmelCase						=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__snake_case    ,    torchify=__snake_case       )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    ,    torch.Tensor       )

										# Test whether the method "pad" and calling the image processor return the same tensors
										lowerCAmelCase						=					image_processing_a.pad(__snake_case    ,    return_tensors="""pt"""       )
										lowerCAmelCase						=					image_processing_a(__snake_case    ,    return_tensors="""pt"""       )

										self.assertTrue(
										    torch.allclose(encoded_images_with_method["""pixel_values"""]    ,    encoded_images["""pixel_values"""]    ,    atol=1e-4       )       )


						@slow
						def 	__lowercase			(				self					:      Optional[Any]       ):
										# prepare image and target
										lowerCAmelCase						=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,    """r"""       ) as f:
														lowerCAmelCase						=					json.loads(f.read()       )

										lowerCAmelCase						=					{'image_id': 3_9769, 'annotations': target}

										# encode them
										lowerCAmelCase						=					YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""       )
										lowerCAmelCase						=					image_processing(images=__snake_case    ,    annotations=__snake_case    ,    return_tensors="""pt"""       )

										# verify pixel values
										lowerCAmelCase						=					torch.Size([1, 3, 800, 1066]       )
										self.assertEqual(encoding["""pixel_values"""].shape    ,    __snake_case       )

										lowerCAmelCase						=					torch.tensor([0.2796, 0.3138, 0.3481]       )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,    __snake_case    ,    atol=1e-4       )       )

										# verify area
										lowerCAmelCase						=					torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,    __snake_case       )       )
										# verify boxes
										lowerCAmelCase						=					torch.Size([6, 4]       )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,    __snake_case       )
										lowerCAmelCase						=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,    __snake_case    ,    atol=1e-3       )       )
										# verify image_id
										lowerCAmelCase						=					torch.tensor([3_9769]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,    __snake_case       )       )
										# verify is_crowd
										lowerCAmelCase						=					torch.tensor([0, 0, 0, 0, 0, 0]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,    __snake_case       )       )
										# verify class_labels
										lowerCAmelCase						=					torch.tensor([75, 75, 63, 65, 17, 17]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,    __snake_case       )       )
										# verify orig_size
										lowerCAmelCase						=					torch.tensor([480, 640]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,    __snake_case       )       )
										# verify size
										lowerCAmelCase						=					torch.tensor([800, 1066]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,    __snake_case       )       )




						@slow
						def 	__lowercase			(				self					:      Dict       ):
										# prepare image, target and masks_path
										lowerCAmelCase						=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,    """r"""       ) as f:
														lowerCAmelCase						=					json.loads(f.read()       )

										lowerCAmelCase						=					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

										lowerCAmelCase						=					pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

										# encode them
										lowerCAmelCase						=					YolosImageProcessor(format="""coco_panoptic"""       )
										lowerCAmelCase						=					image_processing(images=__snake_case    ,    annotations=__snake_case    ,    masks_path=__snake_case    ,    return_tensors="""pt"""       )

										# verify pixel values
										lowerCAmelCase						=					torch.Size([1, 3, 800, 1066]       )
										self.assertEqual(encoding["""pixel_values"""].shape    ,    __snake_case       )

										lowerCAmelCase						=					torch.tensor([0.2796, 0.3138, 0.3481]       )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,    __snake_case    ,    atol=1e-4       )       )

										# verify area
										lowerCAmelCase						=					torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,    __snake_case       )       )
										# verify boxes
										lowerCAmelCase						=					torch.Size([6, 4]       )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,    __snake_case       )
										lowerCAmelCase						=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,    __snake_case    ,    atol=1e-3       )       )
										# verify image_id
										lowerCAmelCase						=					torch.tensor([3_9769]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,    __snake_case       )       )
										# verify is_crowd
										lowerCAmelCase						=					torch.tensor([0, 0, 0, 0, 0, 0]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,    __snake_case       )       )
										# verify class_labels
										lowerCAmelCase						=					torch.tensor([17, 17, 63, 75, 75, 93]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,    __snake_case       )       )
										# verify masks
										lowerCAmelCase						=					82_2873
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,    __snake_case       )
										# verify orig_size
										lowerCAmelCase						=					torch.tensor([480, 640]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,    __snake_case       )       )
										# verify size
										lowerCAmelCase						=					torch.tensor([800, 1066]       )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,    __snake_case       )       )










'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase:   Dict					=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:   Union[str, Any]					=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:   Union[str, Any]					=		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:   Optional[Any]					=		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:   int					=		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase:   List[Any]					=		[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def  lowerCamelCase__			(		_A							):
  a		:   Optional[Any]	  =	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,     _A							)
  return [m.group(0							) for m in matches]
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:   Union[str, Any]	  =	{
      config.replace('Config'					,     ''							): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  a		:   Union[str, Any]	  =	collections.defaultdict(_A							)
  a		:   Optional[Any]	  =	collections.defaultdict(_A							)
  a		:   int	  =	collections.defaultdict(_A							)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_A							):
    a		:   Optional[Any]	  =	None
    if _re_tf_models.match(_A							) is not None:
      a		:   List[str]	  =	tf_models
      a		:   str	  =	_re_tf_models.match(_A							).groups()[0]
    elif _re_flax_models.match(_A							) is not None:
      a		:   Optional[int]	  =	flax_models
      a		:   List[Any]	  =	_re_flax_models.match(_A							).groups()[0]
    elif _re_pt_models.match(_A							) is not None:
      a		:   int	  =	pt_models
      a		:   List[str]	  =	_re_pt_models.match(_A							).groups()[0]

    if lookup_dict is not None:
      while len(_A							) > 0:
        if attr_name in model_prefix_to_model_type:
          a		:   Optional[Any]	  =	True
          break
        # Try again after removing the last word in the name
        a		:   List[str]	  =	''.join(camel_case_split(_A							)[:-1]							)

  a		:   Any	  =	set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
  a		:   int	  =	list(_A							)
  all_models.sort()

  a		:   Optional[Any]	  =	{'model_type': all_models}
  a		:   List[str]	  =	[pt_models[t] for t in all_models]
  a		:   str	  =	[tf_models[t] for t in all_models]
  a		:   int	  =	[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  a		:   Any	  =	{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      a		:   Optional[int]	  =	'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      a		:   Union[str, Any]	  =	'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      a		:   Tuple	  =	'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      a		:   str	  =	'AutoTokenizer'

  a		:   str	  =	[processors[t] for t in all_models]

  return pd.DataFrame(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    a		:   Optional[Any]	  =	[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    a		:   Optional[int]	  =	[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_A					,     _A					,     _A							):
      # The type of pipeline may not exist in this framework
      if not hasattr(_A					,     _A							):
        continue
      # First extract all model_names
      a		:   List[str]	  =	[]
      for name in getattr(_A					,     _A							).values():
        if isinstance(_A					,     _A							):
          model_names.append(_A							)
        else:
          model_names.extend(list(_A							)							)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

  return table
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	get_frameworks_table()
  a		:   List[str]	  =	Dataset.from_pandas(_A							)

  a		:   Optional[Any]	  =	hf_hub_download(
      'huggingface/transformers-metadata'					,     'pipeline_tags.json'					,     repo_type='dataset'					,     token=_A							)
  a		:   List[Any]	  =	Dataset.from_json(_A							)
  a		:   Optional[Any]	  =	{
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_A							)							)
  }
  a		:   Dict	  =	update_pipeline_and_auto_class_table(_A							)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  a		:   str	  =	sorted(table.keys()							)
  a		:   List[Any]	  =	pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }							)
  a		:   Optional[Any]	  =	Dataset.from_pandas(_A							)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_A					,     'frameworks.json'							)							)
    tags_dataset.to_json(os.path.join(_A					,     'pipeline_tags.json'							)							)

    if commit_sha is not None:
      a		:   int	  =	(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      a		:   Optional[int]	  =	'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'					,     folder_path=_A					,     repo_type='dataset'					,     token=_A					,     commit_message=_A					,     )
def  lowerCamelCase__			(		):
  a		:   int	  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  a		:   List[Any]	  =	transformers_module.pipelines.SUPPORTED_TASKS
  a		:   str	  =	[]
  for key in pipeline_tasks:
    if key not in in_table:
      a		:   Dict	  =	pipeline_tasks[key]['pt']
      if isinstance(_A					,     (list, tuple)							):
        a		:   List[Any]	  =	model[0]
      a		:   Union[str, Any]	  =	model.__name__
      if model not in in_table.values():
        missing.append(_A							)

  if len(_A							) > 0:
    a		:   List[str]	  =	', '.join(_A							)
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""							)


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		argparse.ArgumentParser()
     parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
     parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
     parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()

     if args.check_only:
          check_pipeline_tags()
     else:
          update_metadata(args.token, args.commit_sha)